Semantic caching algorithm added

examples/retrieval_eval_under_incomplete.py

@@ -225,7 +225,7 @@ def execute(args):
 
     print(final_df.head())
     print(f"Results have been saved to {final_csv_path}")
-    stopJVM()
+    # stopJVM()
     return avg_jaccard_reasoners
 
 

examples/retrieval_with_cache.py

@@ -0,0 +1,57 @@
+
+""" Run the ontolearn/semantic.caching.py with arguments.
+    Output will be csv files showing the performnce of the reasoner with and without cache on the chosen dataset(s).
+    The files are saved in the same directory for further analysis.
+    e.g. If run like this, we will see the performance of EBR withou the semantic cache on the family datasets with
+    all eviction strategies and cache sizes k * num_concepts where k \in [.1, .2, .4, .8, 1.]"""
+
+import argparse 
+import pandas as pd
+from ontolearn.semantic_caching import run_semantic_cache, concept_generator
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--cache_size_ratios', type=list, default=[.1, .2, .4, .8, 1.], help="cache size is proportional to num_concepts, cache size = k * num_concepts")
+parser.add_argument('--path_kg', type=str, default=["KGs/Family/family.owl"])
+parser.add_argument('--path_kge', type=list, default=None)
+parser.add_argument('--name_reasoner', type=str, default='EBR', choices=["EBR",'HermiT', 'Pellet', 'JFact', 'Openllet'])
+parser.add_argument('--eviction_strategy', type=str, default='LRU', choices=['LIFO', 'FIFO', 'LRU', 'MRU', 'RP'])
+parser.add_argument('--random_seed_for_RP', type=int, default=10, help="Random seed if the eviction startegy is RP")
+parser.add_argument('--cache_type', type=str, default='cold', choices=['hot', 'cold'], help="Type of cache to be used. With cold cache we initialize the cache with NC, NNC and existantial concepts")
+parser.add_argument('--shuffle_concepts', action='store_true',help="If set, we shuffle the concepts for randomness")
+args = parser.parse_args()
+
+def get_cache_size(list_k, path_kg):
+
+    data_size = len(concept_generator(path_kg))
+
+    return [max(1, int(k * data_size)) for k in list_k]
+
+
+results = []
+detailed_results = []
+for path_kg in args.path_kg:
+    for cache_size in get_cache_size(args.cache_size_ratios, path_kg):
+        for strategy in ['LIFO', 'FIFO', 'LRU', 'MRU', 'RP']:
+            result, detailed = run_semantic_cache(
+                path_kg=path_kg,
+                path_kge=args.path_kge,
+                cache_size=cache_size,
+                name_reasoner=args.name_reasoner,
+                eviction=strategy,
+                random_seed=args.random_seed_for_RP,
+                cache_type=args.cache_type,
+                shuffle_concepts=args.shuffle_concepts
+            )
+            results.append(result)
+            detailed_results.append(detailed)
+
+    data_name = result['dataset']
+    df = pd.DataFrame(results)
+    all_detailed_results = pd.DataFrame([item for sublist in detailed_results for item in sublist])
+    print(df)
+
+    # Save to CSV
+    df.to_csv(f'caching_results_{data_name}/cache_experiments_{args.name_reasoner}_{data_name}_{args.cache_type}.csv', index=False)
+    all_detailed_results.to_csv(f'caching_results_{data_name}/detailled_experiments_{args.name_reasoner}_{data_name}_{args.cache_type}.csv', index=False)
+